SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2541–2550 of 3874 papers

TitleStatusHype
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and BaselineCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images—0
Context-self contrastive pretraining for crop type semantic segmentationCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
NU-Wave: A Diffusion Probabilistic Model for Neural Audio UpsamplingCode1
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified